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"I found the book very interesting, and will consider it as a supplement for my graduate statistics course, Managerial Statistical Analysis. This book covers a number of topics that graduate students need, but which we currently do not cover in our present format, such as questionnaire design, validity and reliability." --Stanley A. Taylor, School of Business Administration, California State University, Sacramento "The author uses plenty of examples and the text is well written and easy to understand. The exercises at the end of each chapter were reasonable and useful." --David J. Solomon, Michigan State University "This book covers the most important topics involved in statistical research. It is informative, and topics are presented in a way that is easy to understand." --J.L. Madrigal, Department of Statistics, Brigham Young University Aimed at students who do not plan to be academic researchers but rather who will use measurement and comparison methods in their work, this book teaches readers methods for sampling, data gathering, developing questionnaires, reliability and validity, and quantitative and qualitative measurement methods through the use of frequent examples and exercises. In addition, the book covers the use of quality improvement tools and techniques in measurement. Readers who do measurement activity in their workplace will find that this book provides them with all the research, statistical, and qualitative tools that they′ll need. Given the trend toward improving quality and customer satisfaction, measurement has become an increasingly necessary skill for business managers to possess in order to assess change and improvement. This book provides upper-division undergraduate students in business and management with these general measurement principles. Through the use of frequent business examples and exercises, the contributors cover such topics as sampling methods for data gathering, developing questionnaires, measuring instruments, quantitative and qualitative measurement methods, items analysis, reliability and validity, measurement for quality, and computer uses in data analysis. Professors who have been frustrated with the lack of measurement materials for their business and management students will find this text answers their needs.
Now updated with new measurement methods and new examples, How to Measure Anything shows managers how to inform themselves in order to make less risky, more profitable business decisions This insightful and eloquent book will show you how to measure those things in your own business, government agency or other organization that, until now, you may have considered "immeasurable," including customer satisfaction, organizational flexibility, technology risk, and technology ROI. Adds new measurement methods, showing how they can be applied to a variety of areas such as risk management and customer satisfaction Simplifies overall content while still making the more technical applications available to those readers who want to dig deeper Continues to boldly assert that any perception of "immeasurability" is based on certain popular misconceptions about measurement and measurement methods Shows the common reasoning for calling something immeasurable, and sets out to correct those ideas Offers practical methods for measuring a variety of "intangibles" Provides an online database (www.howtomeasureanything.com) of downloadable, practical examples worked out in detailed spreadsheets Written by recognized expert Douglas Hubbard—creator of Applied Information Economics—How to Measure Anything, Third Edition illustrates how the author has used his approach across various industries and how any problem, no matter how difficult, ill defined, or uncertain can lend itself to measurement using proven methods.
This book provides a comprehensive introduction to the theory and practice of diagnostic classification models (DCMs), which are useful for statistically driven diagnostic decision making. DCMs can be employed in a wide range of disciplines, including educational assessment and clinical psychology. For the first time in a single volume, the authors present the key conceptual underpinnings and methodological foundations for applying these models in practice. Specifically, they discuss a unified approach to DCMs, the mathematical structure of DCMs and their relationship to other latent variable models, and the implementation and estimation of DCMs using Mplus. The book's highly accessible language, real-world applications, numerous examples, and clearly annotated equations will encourage professionals and students to explore the utility and statistical properties of DCMs in their own projects. This book will appeal to professionals in the testing industry; professors and students in educational, school, clinical, and cognitive psychology. It will also serve as a useful text in doctoral-level courses in diagnostic testing, cognitive diagnostic assessment, test validity, diagnostic assessment, advanced educational measurement, psychometrics, and item response theory
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "The effort of Professor Fuller is commendable . . . [the book] provides a complete treatment of an important and frequently ignored topic. Those who work with measurement error models will find it valuable. It is the fundamental book on the subject, and statisticians will benefit from adding this book to their collection or to university or departmental libraries." -Biometrics "Given the large and diverse literature on measurement error/errors-in-variables problems, Fuller's book is most welcome. Anyone with an interest in the subject should certainly have this book." -Journal of the American Statistical Association "The author is to be commended for providing a complete presentation of a very important topic. Statisticians working with measurement error problems will benefit from adding this book to their collection." -Technometrics " . . . this book is a remarkable achievement and the product of impressive top-grade scholarly work." -Journal of Applied Econometrics Measurement Error Models offers coverage of estimation for situations where the model variables are observed subject to measurement error. Regression models are included with errors in the variables, latent variable models, and factor models. Results from several areas of application are discussed, including recent results for nonlinear models and for models with unequal variances. The estimation of true values for the fixed model, prediction of true values under the random model, model checks, and the analysis of residuals are addressed, and in addition, procedures are illustrated with data drawn from nearly twenty real data sets.
The stray radiation field from high energy particle accelerators is comprised of several components, e.g., fast and thermal neutrons, x- and gamma rays, proton recoils, and at sufficiently high energies, heavy particle recoils and mesons. In order to determine permissible occupancy times for personnel in this radiation field, a measurement is required both of the intensity and the energy of the different components constituting the spectrum. Then, from biological or clinical experience concerning the relative effectiveness of different radiations in producing deleterious effects, the biological hazard may be inferred. This outlines very briefly the radiation problem with an accelerator in operation.
This book is a valuable read for a diverse group of researchers and practitioners who analyze assessment data and construct test instruments. It focuses on the use of classical test theory (CTT) and item response theory (IRT), which are often required in the fields of psychology (e.g. for measuring psychological traits), health (e.g. for measuring the severity of disorders), and education (e.g. for measuring student performance), and makes these analytical tools accessible to a broader audience. Having taught assessment subjects to students from diverse backgrounds for a number of years, the three authors have a wealth of experience in presenting educational measurement topics, in-depth concepts and applications in an accessible format. As such, the book addresses the needs of readers who use CTT and IRT in their work but do not necessarily have an extensive mathematical background. The book also sheds light on common misconceptions in applying measurement models, and presents an integrated approach to different measurement methods, such as contrasting CTT with IRT and multidimensional IRT models with unidimensional IRT models. Wherever possible, comparisons between models are explicitly made. In addition, the book discusses concepts for test equating and differential item functioning, as well as Bayesian IRT models and plausible values using simple examples. This book can serve as a textbook for introductory courses on educational measurement, as supplementary reading for advanced courses, or as a valuable reference guide for researchers interested in analyzing student assessment data.
Through examples and exercises, this handy student guide teaches methods for sampling, data gathering, developing questionnaires, reliability and validity, and quantitative and qualitative measurement. In addition, the book explains the use of quality improvement tools and techniques in measurement. It will be invaluable in any graduate statistics course, particularly for those in business administration and management.